{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 时间序列绘图\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from dateutil.parser import parse\n",
    "from pandas import DataFrame, Series\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>7.86</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-05</th>\n",
       "      <td>8.18</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-06</th>\n",
       "      <td>8.12</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-07</th>\n",
       "      <td>7.77</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  MSFT   XOM\n",
       "1990-02-01  7.86  0.51  6.12\n",
       "1990-02-02  8.00  0.51  6.24\n",
       "1990-02-05  8.18  0.51  6.25\n",
       "1990-02-06  8.12  0.51  6.23\n",
       "1990-02-07  7.77  0.51  6.33"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px_all = pd.read_csv('../data/stock_px.csv', parse_dates=True, index_col=0)\n",
    "close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]\n",
    "close_px.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>7.86</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-05</th>\n",
       "      <td>8.18</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-06</th>\n",
       "      <td>8.12</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-07</th>\n",
       "      <td>7.77</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  MSFT   XOM\n",
       "1990-02-01  7.86  0.51  6.12\n",
       "1990-02-02  8.00  0.51  6.24\n",
       "1990-02-05  8.18  0.51  6.25\n",
       "1990-02-06  8.12  0.51  6.23\n",
       "1990-02-07  7.77  0.51  6.33"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px = close_px.resample('B').ffill() # 用工作日重采样\n",
    "close_px.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20040495ef0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hfVvNdfPcCt/kub3CzH8TvKB3tE77xaLAdt9e6VFXVqAXkYySnZUVdi74grws1v/s7FZp\nFf16dete4f54pCIFehHJGH+5/AQONrSwdlfo2kh52VkhXSmPqyjp8r3OnjAIE2Ga4lSiQC8iGWPU\nwD4sc/rIt7hbT3UQLig3u0NnoIykseVI/mGlob14UpUCvYikrZ01oUsGHjOgN+AbDAVQ1rv9AU1N\nrs7Ne7PtYENg++snHtWpc5NJgV5E0tbmfYdb7Vvg0umVgK9Gv6u2kX31LQwuLgh7fmdr9DUNvqmJ\nH75iOhX9VKMXEYm7J9/f0Wo/y0COM5Ok22t5eMkWAHbVNoU9f/m2mk7dr6ahBYCSwtwOcqYWBXoR\nSVtjBvkW+Jj/jRO49bPjGFzci+wsX1jzWkt9U+j0B4vnnRGYuiB4+oRo+Gv0xb0U6EVEEiIvxxfC\nJlYUc/nM4UDrGv2oQaErPQ0t6cWHt53NSUf3B3zz1kSrptFXo0+HicyCKdCLSNrZV99Mi9tLg9ML\nJniq4iwn0Hs8Fpe7/ZetJ47wBfrOjI6taXCRk2UoykuP/vN+6TGsS0QkyNTb/8mccQNpbPFgDK1m\nkTxSo/fy8NKtADzyrRkh1/B/G2hxdzxVcZPLw31vbuLTuiZKCnPTpv+8nwK9iKSVVdtrAViwdncg\nLTjw+pf181rLhj31AMwYURpynXwn0De7vRTlR77n35du5Zev+hYWDzdDZqpT042IpJUlm/ZHPB7c\nRu8XfrCUr1ln4976Du/ZFNQN81CE+e1TlQK9iKSVtzfui3jc30bv7qBHzWvOoiPRTGyW18VVqFJF\n+n0HEZEebYXTdNMef42+xeNlRFkR44aETk8cnM8bxcvY+jSsxQdL7z9TItLjTBhaHPG4v5XmxsdX\n0ujytDvDZHBbfkcOHG4JbB9/VHosNhJMgV5E0sobH+9ttT97TOvFP+qbfe3pG/bUs6u2iWed+ejb\n8rfbr98duY3+oXc+4aF3tjjnwF+vmNaVYieVmm5EJK35B0r5Nba0bmZpbqcvvVOh7/Dl6o+e8S0b\neHR5Ea/956yuFTLJVKMXkbR28siyVvtnjBnYan9An/B9JzsxIBaAjXsPd5wpRSnQi0ja2HMo/ORk\nwcrbBPZDYea7Abj2MyNjUqZ0oEAvImmh2e1h2s9e6/R5j307dFQswORK30vVCycNafdc/4CrdKdA\nLyJpIfil6VH9fXPBH1cRuQcOQFnv9oe9Di3pRU6EPvK3PL26EyVMXQr0IpIWrn1seWB7y/6GCDlh\n5jH9A9vtvYz1i9S98p2gUbiRav6pToFeRNJCVtAsBv5JytoL0Q99c3pgu6p/+ytBZWVFuEiQyZUl\n/Pj88VGUMjWpe6WIpIVzJg7m493ruefSKUysKKZ3fg7XzRkVNm92luGTO8/t8JoGE9WAqSe/c1La\nzVgZTDV6EUkLd/1zPeAL+L3zc1j9k7nMGj2gg7Mi23qggaeXhx9QFSydgzwo0IuIUDXvBZpc4RcK\n//apIxJcmthToBeRlFfX5Ir7PeYv/qTV/sKPfLNbrtjeuQXEU5ECvYikvFUdzFgZCw1tpk64fP57\nACzZdCDu9463DgO9MeYBY8weY8zqoLRSY8wCY8x653c/J90YY+42xmwwxqw0xkyJZ+FFpGeobfTV\n6Od/44S43WNyZUmr/UJnXdjK0vZ77aSLaGr0fwHOapM2D3jNWjsSeM3ZBzgbGOn8XAn8ITbFFJGe\nbGdNIxAajGPJ43S3b3J5uPWZ1Zw1YRAAd39lctzumSgddq+01r5hjKlqk3wBMMvZfhBYBPyXk/6Q\ntdYCS4wxJcaYwdbaXbEqsIj0PK+s+RSA4l65cbvHwo/2MLi4gPe3HuRBZ1piiG70barrahv9QH/w\ndn77+zgNBbYF5dvupIUwxlxpjKk2xlTv3bs3XBYR6aEaWtzYoP7t731yEIhvN8e/L93Keb99i8PN\nR3rfFORmpX3XSoj9y9hwTyTsaARr7b3W2qnW2qnl5eUxLoaIpKt/vLeNcT96hXlPrMLjtdz/1mZ6\n5WZz6qjYx4nBxQUhac1BC4E3uSJPn5AuuhrodxtjBgM4v/c46duBYUH5KoCORyOIiDheXO1r6X2s\nehsvrtrFbc+vpdHlYURZUczvFW7CM//ArEzS1UD/LHCZs30Z8ExQ+ted3jczgFq1z4tINOqb3VTN\ne4HSojwAzhw3kHW76gLH49E+n5sduVnmoikVMb9nMkTTvfIR4B1gtDFmuzHmCuBOYI4xZj0wx9kH\neBHYBGwA/gx8Ny6lFpGMc8mflwDw5Ps7ANhd18Q9izYGjq/aEfu+9LkRpigGeOL97TG/ZzJE0+vm\nK+0cmh0mrwWu6m6hRKTnmTi0mJVBA6NWBG0bA3deNDHm95w+vJSlm9N/QFRHNDJWRGJu+bYafvta\n59q6N0VYk3XzHecyoE/oi9PuuuYzo3jl2lPbPd47PzMm+FWgF5GYu/D3i/nfBR/j8kTXa8Va22qR\nj2BXnDw8lkVrJTvLMHpQn5D0kkLf+4CO2vDThQK9iMRNTUN0k5ENv+lFgJCeNcPLirjp7DExL1d7\nnr16JgBFeTn0zs/h5nPHJeze8ZQZ30tEJCW1N/Vve4aVFuLyejl99AB+esGEOJUq1Ikj+vPOpv30\nK/T1+PF4Lat/Mjdh9483BXoRiZv3tx5kWAeTglXNeyGwfeb4gVw6fVq8ixXiL988odXasqeMLEt4\nGeJJgV5EYsZayxUPVgf2r3l0ORdMCjsLChBa479kWmXcyhZJfk42+Tm+2SrfuOF0BhaHDqRKZ2qj\nF5GY+dfHe3n9wz2t0vxTCuypawpMN+z3v69+1Go/FeaVqexfGAj6mUI1ehGJmfvf2hySNvaWlynM\ny6G+2bewh3/R7vpmN6t3+Ea+lvXOY8H3T0tcQXsYBXoRiZk31+8DYMaI0sDKTF5LIMj71TS0MOmn\nCwDf1AbVN89JbEF7GDXdiEjMPXzF9HaPuT1eHl92ZGqBts05EnsK9CISE/62+CHFBeREmEPm6eU7\nKe9z5GVnPJcHFB8FehGJiWpncZAZR/cHaHdqgev/b0VgIFV5n3xOHzMgbD6JHQV6EYmJS+9bCsAZ\nTuAePagPm+84J3D85nPHBrZfXu1bGnDh9bMSV8AeTIFeRGJq1ugjNXRjDEOcVZwun3lkzpp3Nu2n\nT0FOxkwalur0lEWk2w4ebglstw3eT181kzW76sjOMswZN5AFa3cDcKipdU8ciR/V6EWkWz7Zd5jJ\nty1o9/iAvgWc7tTy7/3a8ZwWh7VfJTIFehHpllm/XBTYvv7MURHzGmP4y+XqZZNoCvQiEjPfnXVM\nh3n80xyMHhg6D7zEh9roRaRLvF7LiB+8GNg/rqKYrKzo5qpZctNsehco/CSKavQi0q41O2v51kPV\nHGoKHb267WBDYPvmc8fyzNUnR33dQcUF6nGTQHrSItKuc+9+C4CJP34VgA9vO4uC3GwaWzyc9otF\ngXxfnXFUMoonUVKgF5GordtVx1/f2cJFx1cE0pb+YDYFuZk1rW+mUaAXkbBWbq8JSbvh8ZVs2FPP\nkx/sAOCJ75zEwL4FiS6adJLa6KVH8Xpt2AAmoZZv8z2nin69+M6sowHYsKe+VZ5jynsnvFzSeQr0\n0qM88t5Wzv/d4pCVjeSIR97dyrItB/jRM2sA39J6XwhqqglWXJibyKJJF6npRnqMZreHHz61GoDf\nvr6BueMHMWFoceC4tZb1e+oZ1YP7d3u9lpueXNUqLSvLMKKsiIF989ld18yXpg7j+rmjW001LKlN\ngV4yWl2Ti5wsQ2FeDrc/v67VsfN++xaf3HkuzW4PDc0e5r/9CXe/th44stxdT7NpX+ummbu+NAnw\nDXJa+oPPYK0N7Ev6UKCXjHas0y0wN9vg8tiQ47UNLo776auJLlbKumfhRgD6FuRw32UnMG14aavj\nCvDpSW30krG83iOBPVyQB9oN8h9+WheXMqWy3XVNgd40L15zSkiQl/SlQC8ZyVrLd//2fkj6H796\nPB/ffjZfnBr+5aLfo+9ui1fRUtaf/rUJgC9OraCiX2GSSyOxpEAvGenA4RZeXvNpq7TyPvnMHT+Q\nvJws7vj8sWHPO3vCIABOHVUW9zKmmgff+QSAH58/PqnlkNhTG71klBXbarjg94u5dHplIO3NG0/n\n6w+8y8P/Nj3QxpwdNPnWT84fz9dmHMWOmkYONrTw0upP8XoTXvSkWrblAB6vZUplCYV5CguZJmP+\ni76y5lNWba/l+rmjO3VecK8MSX8X/H4xAH9buhXwBflhpYVh1ybd9N/nsGlfPccM8HWnHFZayOEW\n36pHLk9mR/oml4eaBhflffK55M9LWLr5AACjB/VNcskkHtI+uh1udjP+1lcC+xOG9uWsCYM7PO/p\nD3Zw7WPLA/vXzB7JORMHs7uuiZOPKYs43erqHbWc99u3+N0lkznv2CHd+wASM2t3hr5AHVbafltz\nVpYJBHm/3Gxfa2ZLBgb6JpeHQ01u+hTkMOaWl0OOX3/mqKjmk5f0Y/z9YpNp6tSptrq6utPnHWpy\nBWbVC7bmJ3Mpys/BWsvPX/6IuiYXN84dzT+qt7F2Zx2XnVTF5+55O+K1P779bPJyjrzCcHm8jPzh\nS+3mf/zfT+QLf3wH8E3ylIz5P7xei9dasoyJel7wdFbX5KIoL4f6JnfY3jOL553B0JJenbrmtgMN\nnPI/C/nlxce1Oxp07c46zrn7Td644XQq+xeyfvchhpUWtjuxV7PbwwsrdzG5sh+7ahrZd7gFay2n\njiynX1Fep8rXkYOHW1pd01pLo8vDuB+9EuEsePiK6Zw8sue9l0h3xphl1tqpHeZLpUDv9Vqa3V56\n5WXj8ni5Z+FGThlVxuRhJa3671prWb2jjs/+7q1A2vzLT+Dy+e8F9m+YO5pfvBJ5mPtXpg3je7NH\nsr++hfN++1bEvG0V5mXT0OIJe+x7s0dy3ZzIS6p1xsurP2X7wQa+OuMoPvr0EJfNf5ezxg/i3087\nmqqyIjxeyzfmv8ub6/e1Ou+rMyq56eyxFOZlc7jFkxbzfy/6aA/9i/KZWFHcKv03/1zPgcPN/PDc\nceTlZFHf7GbCraHB69unjeA/zhiJx2O7NDx/R00jM+98HYD53ziB08f41jr9/cINTBhazGUPvBv2\nvF652ay77azAfttvmu1Z8aMzuzyNgNdreXH1Lj7cdYjfLdzQ6tjwsiKmDy/l0ffC9x4q653Hc/9x\nMtlZhr4FuZp9Mk2lVaCfNOV4ay68g4MNoYsb+H1+8lB+9aVJfLz7EGf++o1A+klH9+fv35oBwLMr\ndvK9Rz7o8H7Dy4q4dHol/3bKiFbpHq/FQKtVc4IdN6yEp75zUqC23OTyUNfk4t8erGbl9lqumT2S\n3zgjK88/bgh3f2Vy4FxrLXWNborys9m49zB/emMjSzbuxxjDnRdN5ISq0lb/2N5cv5dnl+/k/5Zt\n7/DzdMYJVf248awxnFCVWn2kd9U2cuIdr3frGrH4JtXs9jD65iPNGpMrS1izoy7qppxTRpaF/MHt\nSJ+CHJbdPCfwDXLDnnr21TczY0T/kLzWWu58+cNAV8hoHVtRzJ+/PpWSwlxys7J6xDe+niCtAn3+\n4JF28GV3Ae2PYAznZ5+bwCXTKlvV9msbXLz7yQF++cpHTBpWwk8vHE9+TudqK7UNLvbWN3O42U1d\nk4upR5XyzqZ9nDFmYIfnLli7m289dKQZ6ivTKpk4tJgfPLUqwllH9MrNptEV/puC30VTKqhvdvHK\nmt2BtGnDS7luziimDy+lyeWlprGF83+3mL2HmsNeo6x3PhdOGkJp7zwWb9hHRUkhV59xDMNKC7HW\nsqu2CY/X8vbGfRhjmDCkmBHlRR3W/B5esoWdNY3cMHc0/vFK2W2CyrubD/DFP71DXnZWp9rCwz2b\n564+mcr+hRgDfQtiM8HWXxZv5sfPrY2YZ+H1s3jqgx3c/dp6vjPraP6waGPYfHd8fiIV/Xpx4oj+\nWI68AwComvdCh2Up653P/sPN9M7P4VCTu918P/7sOHrlZXPiiDI27z/Mfz2+ki8cX8F1c0ZhjEa0\nZqqkBnpjzFnAb4Bs4D5r7Z2R8hdXjrG/e+xlLpl+VEhQAPjn2t18++FleJzIcduFE7h0WmXK1kra\nvuhtz5s3no7L4+WWZ1azeMP+sMdrG130KcjhqP5FIce9Xt8kXFVlhe3+MWts8ZCVBfk52SF/hLri\nM2MHcOb4QdQ1uliy6QC52Yat4GU8AAAHAklEQVSLp1bQ4vYydnDfVqsO+V0+s4rzjh3Cpfct4ZSR\n5SxYuzv0wkBJYS5PfXcmb2/cx9HlvXF7LEX52Xzunrd58XunMG5IXxpa3Nzy9BrOnzSE8UP6UtY7\nfhNr+V+6Azx91UwmDSuJmN//HueEqn48euWJYf9fDlbf7OaehRv4+7tbqWnn2+yEoX1ZvcP3kjk/\nJ4tmt5e+BTk89x8nU9Y7n6I0aI6T+ElaoDfGZAMfA3OA7cB7wFeste1WkaJ5Gev1Wh5euoVZowZQ\n2T+1R+15vZYlm/czbnBfnnx/B0s372fu+EF8fkoF1lq8NrSWC76v5dVbDlLSK5cR5b07DBTd4fJ4\neWLZdnbXNVNVVsiAPgV8+6/V1Dm1xs9NHspTH+zgc5OHMqXS947kl69+1G5A6qz/uehYThlVxuDi\nXjS7PZ3+1tVTNbk85GQZcrI11lGSG+hPBH5srZ3r7N8EYK29o71zutrrRmJvf30zpUV5Yb/quzxe\nVmyr4ZP9DfQrzKWkMJehJYXsqm3k7Y37+WDrQU46uozLZ1ZhjMFay4rttXi8Xl5ds5v83Gy+/5mR\nakYQiZFoA308vvcNBYJf9W8HprfNZIy5ErgSoLKysu1hSZL+EZpCcrOzmFpVytQ2L3IHFRcwubJf\nSH5jTKC54/ijUuvlr0hPEo/vf+GqayFfG6y191prp1prp5aXl8ehGCIiAvEJ9NuBYUH7FcDOONxH\nRESiEI9A/x4w0hgz3BiTB3wZeDYO9xERkSjEvI3eWus2xlwNvIKve+UD1to1sb6PiIhEJy6dcK21\nLwLhh5eKiEhCqTOuiEiGU6AXEclwCvQiIhkuJSY1M8YcAiLPKRxeMVDbjVt35/xk3rsS2JqE+3b3\nfD2z9Lp3d55Zd++tZxad0dbaPh3mstYm/Qeo7uJ593bzvl0+P8n33pum5dYzS697d/mZJfNz96Rn\nFm3sTPemm+eSeH4y712TpPt293w9s/S6d3eeWXfvrWcWQ6nSdFNto5iYR3z0vDpPz6zz9Mw6L9HP\nLNr7pUqN/t5kFyDN6Hl1np5Z5+mZdV6in1lU90uJGr2IiMRPqtToRUQkThToU4Ax5gFjzB5jzOqg\ntOOMMe8YY1YZY54zxvR10vOMMfOd9BXGmFlB5ywyxnxkjFnu/AxIwsdJCGPMMGPMQmPMOmPMGmPM\nNU56qTFmgTFmvfO7n5NujDF3G2M2GGNWGmOmBF3rMif/emPMZcn6TPEW42fmCfr/LGMnLezCMxvj\n/LttNsZc3+ZaZzn/PjcYY+Yl9IN0pyuQfmLzA5wKTAFWB6W9B5zmbH8TuM3ZvgqY72wPAJYBWc7+\nImBqsj9Pgp7ZYGCKs90H3/KV44D/AeY56fOAnzvb5wAv4VsvYQaw1EkvBTY5v/s52/2S/flS+Zk5\nx+qT/XlS9JkNAE4AfgZcH3SdbGAjMALIA1YA4xL1OVSjTwHW2jeAA22SRwNvONsLgIuc7XHAa855\ne/B15+pxPSOstbuste8724eAdfhWN7sAeNDJ9iBwobN9AfCQ9VkClBhjBgNzgQXW2gPW2oP4nvVZ\nCfwoCRPDZ9ZjdPaZWWv3WGvfA9ourjwN2GCt3WStbQEeda6REAr0qWs1cL6zfTFHFnNZAVxgjMkx\nxgwHjqf1Qi/zna/Tt5gesjirMaYKmAwsBQZaa3eB7x8pvhoWhF/icmiE9IzWzWcGUGCMqTbGLDHG\nXEgPEOUza09S/z9ToE9d3wSuMsYsw/eVscVJfwDf/yTVwF3A24DbOXaptXYicIrz87WEljgJjDG9\ngSeAa621dZGyhkmzEdIzVgyeGUCl9fXfvgS4yxhzdIyLmVI68czavUSYtIT9f6ZAn6KstR9aa8+0\n1h4PPIKvfQ9rrdta+31r7SRr7QVACbDeObbD+X0I+Du+r4sZyxiTi+8f39+stU86ybv9zQvO7z1O\nentLXPaopS9j9Myw1vp/b8L3bmhy3AufJJ18Zu1J6v9nCvQpyt9jxhiTBdwM/NHZLzTGFDnbcwC3\ntXat05RT5qTnAufha/7JSE6z1P3AOmvtr4IOPQv4e85cBjwTlP51pyfJDKDW+cr9CnCmMaaf03Pi\nTCct48TqmTnPKt+5ZhkwE1ibkA+RYF14Zu1J7hKryX6rrR8Lvhr7LnwvcLYDVwDX4HvD/zFwJ0cG\nt1Xhm+lzHfBP4CgnvQhfD5yVwBrgN0B2sj9bHJ/Zyfi++q4Eljs/5wD98b2sXu/8LnXyG+D3+L4Z\nrSKodxK+ZrINzs/lyf5sqf7MgJOc/RXO7yuS/dlS6JkNcv4N1+HrKLEd6OscO8f597wR+GEiP4dG\nxoqIZDg13YiIZDgFehGRDKdALyKS4RToRUQynAK9iEiGU6AXEclwCvQiIhlOgV5EJMP9P/AU6AuZ\nNO0LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200404954a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px['AAPL'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20042749f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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juqI10fDcmmPUDv1fnpWH0jmZXcaMfj6M7OZGoKs1i9YdJ7e0mqX3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200427411d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px.loc['2009'].plot() # 定位到2009年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200427ceef0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x200427fc240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appl_q = close_px['AAPL'].resample('Q-DEC').ffill() # 季度价格变化\n",
    "appl_q.loc['2009':].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
